{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Setup Working Directory\n",
    "\n",
    "import os\n",
    "## Windows System Path\n",
    "FolderList = [xx+\"Dropbox (Bank of Canada)\\\\Research Projects\\\\OHANK\\\\Empirics\\\\\" \\\n",
    "              for xx in [\"E:\\\\\",\"B:\\\\\",\"/mnt/b/\"]]\n",
    "for Folder in FolderList:\n",
    "    if os.path.exists(Folder):\n",
    "        os.chdir(Folder)    \n",
    "\n",
    "## Output Folder\n",
    "OutputFolder = 'TableGraph/'\n",
    "if not os.path.exists(OutputFolder):\n",
    "    os.makedirs(OutputFolder)\n",
    "# End of Section: Setup Working Directory\n",
    "###############################################################################\n",
    "\n",
    "#%% Import Moduels\n",
    "\n",
    "## System Tools\n",
    "\n",
    "import numpy as np\n",
    "from collections import OrderedDict\n",
    "import time\n",
    "## I/O Tools\n",
    "import _pickle as pickle\n",
    "## Data Process Tools\n",
    "import pandas as pd\n",
    "# import modin.pandas as pd\n",
    "import datetime\n",
    "## Graphs\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.backends.backend_pdf as figpdf\n",
    "## Statistical Tools\n",
    "import statsmodels.formula.api as sm\n",
    "from statsmodels.tsa.api import VAR\n",
    "from scipy.stats import mstats\n",
    "from scipy.interpolate import interp1d\n",
    "import statsmodels.api as SMAPI\n",
    "from statsmodels.tsa.tsatools import detrend as DeTrend\n",
    "from statsmodels.tsa.filters.hp_filter import hpfilter as HPfilter\n",
    "from statsmodels.tsa.filters.bk_filter import bkfilter as BKfilter\n",
    "## Database API\n",
    "from fredapi import Fred\n",
    "## Numerical API\n",
    "from scipy.interpolate import interp1d\n",
    "## Regular Expression API\n",
    "import re\n",
    "import multiprocessing as mp\n",
    "\n",
    "idx = pd.IndexSlice\n",
    "\n",
    "# from Toolbox_Graph import Graph_PDF, GraphSetup, MultiLine_SinglePlot, NBER_RecessionBar, IRF_SinglePlot\n",
    "# End of Section: Import Moduels\n",
    "###############################################################################\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Cleaning\n",
    "\n",
    "### Generate the Sample\n",
    "\n",
    "## Data\n",
    "SFS_2016 = pd.read_stata('Data/SFS/sfs2016en.dta')\n",
    "\n",
    "## Variables\n",
    "VarInfo = pd.read_excel('Data/SFS/VarDict.xlsx')\n",
    "VarDict = {VarInfo.loc[ii,'Code'].lower(): VarInfo.loc[ii,'Name'] for ii in VarInfo.index}\n",
    "\n",
    "## Sample\n",
    "DS = SFS_2016.rename(columns=VarDict)[VarDict.values()]\n",
    "DS['Asset_Liquid'] = DS['Asset_Deposit']+DS['Asset_MutualFund']+DS['Asset_Bonds']+DS['Asset_Stock']-DS['Debt_CreditCard']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Functions to Compute the Distributional Features\n",
    "\n",
    "def QW_ApproxPoints(qq,qw=[]):\n",
    "    TempInd = ~pd.isnull(qq)\n",
    "    if len(qw)==0:\n",
    "        qw = np.ones(len(qq))/len(qq)\n",
    "    else:\n",
    "        qw = qw[TempInd].values\n",
    "        qw = qw/sum(qw)\n",
    "    \n",
    "    qq = qq[TempInd].values\n",
    "    \n",
    "    OrderInd = np.argsort(qq)\n",
    "    qq = qq[OrderInd]\n",
    "    qw = qw[OrderInd]\n",
    "    return qq, qw\n",
    "\n",
    "def QW_Quantile(xx,qq,qw=[]):\n",
    "    qq, qw = QW_ApproxPoints(qq,qw)\n",
    "    cdf = np.cumsum(qw)\n",
    "    ff = interp1d(cdf,qq,kind='cubic')\n",
    "    return ff(xx)\n",
    "\n",
    "def Dist_byQuantile(DS,qq_name,qw_name='',xx=[0.25,0.50,0.75]):\n",
    "    qq = DS[qq_name]\n",
    "    if qw_name=='':\n",
    "        qw = qq.copy()\n",
    "        qw = 1\n",
    "    else:\n",
    "        qw = DS[qw_name]\n",
    "        \n",
    "    quant = QW_Quantile(xx,qq,qw=qw)\n",
    "    qq, qw = QW_ApproxPoints(qq,qw)\n",
    "    qq_qw = qq * qw\n",
    "    cdf = np.cumsum(qw)\n",
    "    \n",
    "    AccQQ_up = np.zeros(len(xx))\n",
    "    AccQW_up = np.zeros(len(xx))\n",
    "    Total = sum(qq_qw)\n",
    "    for ii in range(len(xx)):\n",
    "        TempInd = cdf>=xx[ii]\n",
    "        AccQQ_up[ii] = sum(qq_qw[TempInd])/Total\n",
    "        AccQW_up[ii] = sum(qw[TempInd])\n",
    "    Dist = pd.concat([pd.Series(AccQW_up,name='AccQW'), \\\n",
    "                      pd.Series(AccQQ_up,name='AccQQ_up'), \\\n",
    "                      pd.Series(quant,name='QQ'),pd.Series(xx,name='Quant')],axis=1)\n",
    "    Dist['AccQQ_down'] = 1-Dist['AccQQ_up']\n",
    "    return Dist\n",
    "\n",
    "def Dist_byHistogram(DS,qq_name,qw_name,bins=50):\n",
    "    TempDS = DS[[qq_name,qw_name]].dropna()\n",
    "    if type(bins)==int:\n",
    "        cutoff = np.linspace(DS[qq_name].min(),DS[qq_name].max(),num=bins-1)\n",
    "        num = bins\n",
    "    elif type(bins)==list: \n",
    "        cutoff = bins\n",
    "        num = len(bins)+1\n",
    "    \n",
    "    cutoffbins = [-np.inf,*cutoff,np.inf]\n",
    "    TempDS['Group'] = pd.cut(TempDS[qq_name],bins=cutoffbins,labels=range(num))\n",
    "    TempDS['QQ'] = TempDS[qq_name]\n",
    "    TempDS['QW'] = TempDS[qw_name]/TempDS[qw_name].sum()\n",
    "    TempDS['QQ_QW'] = TempDS['QQ']*TempDS['QW']\n",
    "    Hist = TempDS.groupby('Group')[['QW','QQ_QW']].sum()\n",
    "    Hist['QQ_left'] = pd.Series(cutoffbins[0:-1])\n",
    "    Hist['QQ_right'] = pd.Series(cutoffbins[1:])\n",
    "    Hist['QQ_mid'] = (Hist['QQ_left']+Hist['QQ_right'])/2\n",
    "    Hist.loc[0,'QQ_mid'] = Hist.loc[0,'QQ_right']\n",
    "    Hist.loc[num-1,'QQ_mid'] = Hist.loc[num-1,'QQ_left']\n",
    "    \n",
    "    return Hist\n",
    "\n",
    "def Dist_GiniCoef(DS,qq_name,qw_name):\n",
    "    TempDS = DS[[qq_name,qw_name]].sort_values(by=qq_name).reset_index(drop=True)\n",
    "    TempDS['QW'] = TempDS[qw_name]/TempDS[qw_name].sum()\n",
    "    TempDS['QQ_QW'] = TempDS[qq_name]*TempDS['QW']\n",
    "    TempDS['QQ_QW'] = TempDS['QQ_QW']/TempDS['QQ_QW'].sum()\n",
    "    TempDS['AccProb'] = TempDS['QW'].cumsum()\n",
    "    TempDS['AccQQ'] = TempDS['QQ_QW'].cumsum()\n",
    "    TempDS['AccProb_'] = (TempDS['AccProb']+TempDS['AccProb'].shift(1))/2\n",
    "    TempDS.loc[0,'AccProb_'] = TempDS.loc[0,'AccProb']/2\n",
    "    TempDS['AccQQ_'] = (TempDS['AccQQ']+TempDS['AccQQ'].shift(1))/2\n",
    "    TempDS.loc[0,'AccQQ_'] = TempDS.loc[0,'AccQQ']/2\n",
    "    Gini = (TempDS['AccQQ_']*TempDS['QW']).sum()/ \\\n",
    "           (TempDS['AccProb_']*TempDS['QW']).sum()\n",
    "    return Gini\n",
    "\n",
    "def QW_Mean(DS,qq_name,qw_name=''):\n",
    "    if qw_name=='':\n",
    "        return DS[qq_name].mean()\n",
    "    else:\n",
    "        Temp = DS[[qq_name,qw_name]].dropna()\n",
    "        Temp[qw_name] = Temp[qw_name]/Temp[qw_name].sum()\n",
    "        return (Temp[qq_name]*Temp[qw_name]).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Distribution of Income\n",
    "Var_Income = 'Income_Market'\n",
    "DS[Var_Income] = DS[Var_Income].astype('float')\n",
    "\n",
    "## Summary Statistics\n",
    "QuantList = [0.01,0.05,0.10,0.25,0.5,0.75,0.90,0.95,0.99]\n",
    "Income_SumStat = DS[Var_Income].describe(percentiles=QuantList)\n",
    "Income_SumStat['Gini'] = Dist_GiniCoef(DS,Var_Income,'StatWeight')\n",
    "\n",
    "## Detailed Quantiles\n",
    "QuantList = list(np.linspace(0.01,0.99,num=99))\n",
    "IncomeDist_byQuant = Dist_byQuantile(DS,Var_Income,'StatWeight',QuantList)\n",
    "TempData = pd.DataFrame({'AccQW':[1,0],'AccQQ_up':[1,0], \\\n",
    "                         'QQ':[Income_SumStat['min'],Income_SumStat['max']], \\\n",
    "                         'Quant':[0,1],'AccQQ_down':[0,1]})\n",
    "IncomeDist_byQuant = IncomeDist_byQuant.append(TempData).sort_values(by='Quant').reset_index(drop=True)\n",
    "## Detailed Histogram\n",
    "HistList = list(np.linspace(0,36*1e4,37))\n",
    "IncomeDist_byHist = Dist_byHistogram(DS,Var_Income,'StatWeight',bins=HistList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.242900e+04\n",
       "mean     8.848976e+04\n",
       "std      1.309005e+05\n",
       "min     -3.400000e+04\n",
       "1%       0.000000e+00\n",
       "5%       0.000000e+00\n",
       "10%      2.800000e+03\n",
       "25%      2.400000e+04\n",
       "50%      6.000000e+04\n",
       "75%      1.150000e+05\n",
       "90%      1.800000e+05\n",
       "95%      2.400000e+05\n",
       "99%      4.972000e+05\n",
       "max      2.600000e+06\n",
       "Gini     4.732718e-01\n",
       "Name: Income_Market, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Income_SumStat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11568.000000\n",
       "mean        10.882559\n",
       "std          1.291958\n",
       "min          3.218876\n",
       "1%           6.214608\n",
       "5%           8.455318\n",
       "10%          9.350102\n",
       "25%         10.373491\n",
       "50%         11.119883\n",
       "75%         11.695247\n",
       "90%         12.154779\n",
       "95%         12.429216\n",
       "99%         13.171154\n",
       "max         14.771022\n",
       "Name: Income_Market, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Temp = np.log(DS[Var_Income])\n",
    "QuantList = [0.01,0.05,0.10,0.25,0.5,0.75,0.90,0.95,0.99]\n",
    "Temp[np.isfinite(Temp)].describe(percentiles=QuantList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24557cf7550>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Temp[np.isfinite(Temp)].hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.528573014462147"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Temp[np.isfinite(Temp)].kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.5316583121280898"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Temp[np.isfinite(Temp)].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.291958465576519"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Temp[np.isfinite(Temp)].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Details of Distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AccQW</th>\n",
       "      <th>AccQQ_up</th>\n",
       "      <th>QQ</th>\n",
       "      <th>Quant</th>\n",
       "      <th>AccQQ_down</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-3.400000e+04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.990190</td>\n",
       "      <td>1.000220</td>\n",
       "      <td>-7.265792e-53</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.980017</td>\n",
       "      <td>1.000220</td>\n",
       "      <td>9.203256e-118</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.970015</td>\n",
       "      <td>1.000220</td>\n",
       "      <td>-1.717461e-176</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.960027</td>\n",
       "      <td>1.000220</td>\n",
       "      <td>3.791740e-243</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.950106</td>\n",
       "      <td>1.000220</td>\n",
       "      <td>-5.251751e-203</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.940032</td>\n",
       "      <td>1.000220</td>\n",
       "      <td>-4.672051e-143</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.930149</td>\n",
       "      <td>1.000220</td>\n",
       "      <td>-9.385513e-73</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.920052</td>\n",
       "      <td>1.000220</td>\n",
       "      <td>-1.517025e-08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.910101</td>\n",
       "      <td>1.000194</td>\n",
       "      <td>4.757447e+02</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.000194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.900014</td>\n",
       "      <td>1.000085</td>\n",
       "      <td>1.197627e+03</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.000085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.890041</td>\n",
       "      <td>0.999855</td>\n",
       "      <td>2.212673e+03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.000145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.880074</td>\n",
       "      <td>0.999479</td>\n",
       "      <td>3.199998e+03</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.000521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.870080</td>\n",
       "      <td>0.998961</td>\n",
       "      <td>4.499904e+03</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.001039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.860003</td>\n",
       "      <td>0.998280</td>\n",
       "      <td>5.750000e+03</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.001720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.850068</td>\n",
       "      <td>0.997439</td>\n",
       "      <td>6.750996e+03</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.002561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.840044</td>\n",
       "      <td>0.996442</td>\n",
       "      <td>8.000045e+03</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.003558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.830071</td>\n",
       "      <td>0.995275</td>\n",
       "      <td>9.151696e+03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.004725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.820418</td>\n",
       "      <td>0.993955</td>\n",
       "      <td>1.041425e+04</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.006045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.810003</td>\n",
       "      <td>0.992338</td>\n",
       "      <td>1.199977e+04</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.007662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.800461</td>\n",
       "      <td>0.990690</td>\n",
       "      <td>1.350000e+04</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.009310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.790058</td>\n",
       "      <td>0.988723</td>\n",
       "      <td>1.450000e+04</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.011277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.780129</td>\n",
       "      <td>0.986677</td>\n",
       "      <td>1.600000e+04</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.013323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.770043</td>\n",
       "      <td>0.984372</td>\n",
       "      <td>1.750000e+04</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.015628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.760049</td>\n",
       "      <td>0.981946</td>\n",
       "      <td>1.850000e+04</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.018054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.750124</td>\n",
       "      <td>0.979409</td>\n",
       "      <td>1.950000e+04</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.020591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.740044</td>\n",
       "      <td>0.976661</td>\n",
       "      <td>2.100000e+04</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.023339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.730067</td>\n",
       "      <td>0.973748</td>\n",
       "      <td>2.200000e+04</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.026252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.720058</td>\n",
       "      <td>0.970651</td>\n",
       "      <td>2.400000e+04</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.029349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.710054</td>\n",
       "      <td>0.967379</td>\n",
       "      <td>2.500000e+04</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.032621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.290028</td>\n",
       "      <td>0.659174</td>\n",
       "      <td>9.000000e+04</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.340826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.280001</td>\n",
       "      <td>0.646708</td>\n",
       "      <td>9.250000e+04</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.353292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.270091</td>\n",
       "      <td>0.634093</td>\n",
       "      <td>9.500000e+04</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.365907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.260008</td>\n",
       "      <td>0.621036</td>\n",
       "      <td>9.750000e+04</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.378964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.250004</td>\n",
       "      <td>0.607751</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.392249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.240162</td>\n",
       "      <td>0.594407</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.405593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.230110</td>\n",
       "      <td>0.580279</td>\n",
       "      <td>1.050000e+05</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.419721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.220001</td>\n",
       "      <td>0.565793</td>\n",
       "      <td>1.100000e+05</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.434207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.210010</td>\n",
       "      <td>0.550894</td>\n",
       "      <td>1.100000e+05</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.449106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.200017</td>\n",
       "      <td>0.535694</td>\n",
       "      <td>1.150000e+05</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.464306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.190031</td>\n",
       "      <td>0.520124</td>\n",
       "      <td>1.150000e+05</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.479876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.180101</td>\n",
       "      <td>0.504008</td>\n",
       "      <td>1.200000e+05</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.495992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.170098</td>\n",
       "      <td>0.487366</td>\n",
       "      <td>1.250000e+05</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.512634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.160028</td>\n",
       "      <td>0.469977</td>\n",
       "      <td>1.300000e+05</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.530023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.150033</td>\n",
       "      <td>0.452236</td>\n",
       "      <td>1.350000e+05</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.547764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.140003</td>\n",
       "      <td>0.433860</td>\n",
       "      <td>1.399996e+05</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.566140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.130044</td>\n",
       "      <td>0.414935</td>\n",
       "      <td>1.449999e+05</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.585065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.120042</td>\n",
       "      <td>0.395214</td>\n",
       "      <td>1.500000e+05</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.604786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.110004</td>\n",
       "      <td>0.374637</td>\n",
       "      <td>1.550000e+05</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.625363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.100012</td>\n",
       "      <td>0.353347</td>\n",
       "      <td>1.600000e+05</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.646653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0.090041</td>\n",
       "      <td>0.331312</td>\n",
       "      <td>1.650000e+05</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.668688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.080076</td>\n",
       "      <td>0.308417</td>\n",
       "      <td>1.750000e+05</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.691583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>0.070052</td>\n",
       "      <td>0.284292</td>\n",
       "      <td>1.800000e+05</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.715708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>0.060042</td>\n",
       "      <td>0.258954</td>\n",
       "      <td>1.900000e+05</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.741046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.050008</td>\n",
       "      <td>0.232255</td>\n",
       "      <td>2.000000e+05</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.767745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.040013</td>\n",
       "      <td>0.203610</td>\n",
       "      <td>2.200000e+05</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.796390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.030135</td>\n",
       "      <td>0.172779</td>\n",
       "      <td>2.400000e+05</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.827221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.020006</td>\n",
       "      <td>0.137715</td>\n",
       "      <td>2.700000e+05</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.862285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.010023</td>\n",
       "      <td>0.096053</td>\n",
       "      <td>3.600000e+05</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.903947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.600000e+06</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        AccQW  AccQQ_up             QQ  Quant  AccQQ_down\n",
       "0    1.000000  1.000000  -3.400000e+04   0.00    0.000000\n",
       "1    0.990190  1.000220  -7.265792e-53   0.01   -0.000220\n",
       "2    0.980017  1.000220  9.203256e-118   0.02   -0.000220\n",
       "3    0.970015  1.000220 -1.717461e-176   0.03   -0.000220\n",
       "4    0.960027  1.000220  3.791740e-243   0.04   -0.000220\n",
       "..        ...       ...            ...    ...         ...\n",
       "96   0.040013  0.203610   2.200000e+05   0.96    0.796390\n",
       "97   0.030135  0.172779   2.400000e+05   0.97    0.827221\n",
       "98   0.020006  0.137715   2.700000e+05   0.98    0.862285\n",
       "99   0.010023  0.096053   3.600000e+05   0.99    0.903947\n",
       "100  0.000000  0.000000   2.600000e+06   1.00    1.000000\n",
       "\n",
       "[101 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IncomeDist_byQuant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f34f24a320>,\n",
       " <matplotlib.lines.Line2D at 0x1f34f24a4e0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(IncomeDist_byQuant['Quant'],IncomeDist_byQuant[['Quant','AccQQ_down']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f34f2f5d30>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "IncomeDist_byHist.plot(kind='bar',x='QQ_mid',y='QW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2790724625901069"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Temp = np.log(DS['Income_Market'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Net Wealth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Distribution of Net Worth\n",
    "Var_Wealth = 'NetWorth_G'\n",
    "\n",
    "## Summary Statistics\n",
    "QuantList = [0.01,0.05,0.10,0.25,0.5,0.75,0.90,0.95,0.99]\n",
    "Wealth_SumStat = DS[Var_Wealth].describe(percentiles=QuantList)\n",
    "Wealth_SumStat['Gini'] = Dist_GiniCoef(DS,Var_Wealth,'StatWeight')\n",
    "\n",
    "## by Quantiles\n",
    "QuantList = list(np.linspace(0.01,0.99,num=99))\n",
    "WealthDist_byQuant = Dist_byQuantile(DS,Var_Wealth,'StatWeight',QuantList)\n",
    "TempData = pd.DataFrame({'AccQW':[1,0],'AccQQ_up':[1,0], \\\n",
    "                         'QQ':[Income_SumStat['min'],Income_SumStat['max']], \\\n",
    "                         'Quant':[0,1],'AccQQ_down':[0,1]})\n",
    "WealthDist_byQuant = WealthDist_byQuant.append(TempData).sort_values(by='Quant').reset_index(drop=True)\n",
    "## by Histogram Grids\n",
    "HistList = [*list(np.linspace(-3*1e4,0-1e3,4)),*list(np.linspace(1e3,700*1e4,36))]\n",
    "WealthDist_byHist = Dist_byHistogram(DS,Var_Wealth,'StatWeight',bins=HistList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.242900e+04\n",
       "mean     8.950960e+05\n",
       "std      1.586026e+06\n",
       "min     -1.110000e+06\n",
       "1%      -2.672220e+04\n",
       "5%       3.250000e+02\n",
       "10%      6.250000e+03\n",
       "25%      8.805000e+04\n",
       "50%      4.360000e+05\n",
       "75%      1.124400e+06\n",
       "90%      2.116650e+06\n",
       "95%      3.024260e+06\n",
       "99%      6.971320e+06\n",
       "max      2.728450e+07\n",
       "Gini     3.354429e-01\n",
       "Name: NetWorth_G, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wealth_SumStat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Details of Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AccQW</th>\n",
       "      <th>AccQQ_up</th>\n",
       "      <th>QQ</th>\n",
       "      <th>Quant</th>\n",
       "      <th>AccQQ_down</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-3.400000e+04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.990051</td>\n",
       "      <td>1.001109</td>\n",
       "      <td>-3.119506e+04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.001109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.980136</td>\n",
       "      <td>1.001435</td>\n",
       "      <td>-1.600528e+04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.001435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.970714</td>\n",
       "      <td>1.001620</td>\n",
       "      <td>-1.026172e+04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.001620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.960013</td>\n",
       "      <td>1.001735</td>\n",
       "      <td>-4.304071e+03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.001735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.950115</td>\n",
       "      <td>1.001771</td>\n",
       "      <td>-9.028671e+02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.001771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.940038</td>\n",
       "      <td>1.001774</td>\n",
       "      <td>2.498986e+02</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.001774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.930589</td>\n",
       "      <td>1.001768</td>\n",
       "      <td>7.956009e+02</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.001768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.920025</td>\n",
       "      <td>1.001753</td>\n",
       "      <td>1.050704e+03</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.001753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.910062</td>\n",
       "      <td>1.001732</td>\n",
       "      <td>1.799650e+03</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.001732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.900498</td>\n",
       "      <td>1.001701</td>\n",
       "      <td>2.616892e+03</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.001701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.890163</td>\n",
       "      <td>1.001654</td>\n",
       "      <td>3.547615e+03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.001654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.880019</td>\n",
       "      <td>1.001592</td>\n",
       "      <td>4.832858e+03</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.001592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.870143</td>\n",
       "      <td>1.001514</td>\n",
       "      <td>5.783996e+03</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.001514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.860086</td>\n",
       "      <td>1.001418</td>\n",
       "      <td>7.280902e+03</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.001418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.850192</td>\n",
       "      <td>1.001304</td>\n",
       "      <td>8.538834e+03</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.001304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.840064</td>\n",
       "      <td>1.001163</td>\n",
       "      <td>1.038621e+04</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.001163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.830114</td>\n",
       "      <td>1.001000</td>\n",
       "      <td>1.225545e+04</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.820311</td>\n",
       "      <td>1.000807</td>\n",
       "      <td>1.417476e+04</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.000807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.810110</td>\n",
       "      <td>1.000572</td>\n",
       "      <td>1.714917e+04</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.000572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.800382</td>\n",
       "      <td>1.000301</td>\n",
       "      <td>2.008779e+04</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.000301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.790019</td>\n",
       "      <td>0.999970</td>\n",
       "      <td>2.368165e+04</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.780017</td>\n",
       "      <td>0.999594</td>\n",
       "      <td>2.723288e+04</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.000406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.770036</td>\n",
       "      <td>0.999162</td>\n",
       "      <td>3.153552e+04</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.000838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.760172</td>\n",
       "      <td>0.998668</td>\n",
       "      <td>3.671931e+04</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.001332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.750063</td>\n",
       "      <td>0.998069</td>\n",
       "      <td>4.324597e+04</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.001931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.740091</td>\n",
       "      <td>0.997392</td>\n",
       "      <td>4.900037e+04</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.002608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.730121</td>\n",
       "      <td>0.996637</td>\n",
       "      <td>5.359078e+04</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.003363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.720118</td>\n",
       "      <td>0.995797</td>\n",
       "      <td>6.065670e+04</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.004203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.710067</td>\n",
       "      <td>0.994848</td>\n",
       "      <td>6.806545e+04</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.005152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.290032</td>\n",
       "      <td>0.784794</td>\n",
       "      <td>7.356027e+05</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.215206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.280114</td>\n",
       "      <td>0.773852</td>\n",
       "      <td>7.592818e+05</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.226148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.270183</td>\n",
       "      <td>0.762416</td>\n",
       "      <td>7.954814e+05</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.237584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.260045</td>\n",
       "      <td>0.750267</td>\n",
       "      <td>8.262094e+05</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.249733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.250103</td>\n",
       "      <td>0.737898</td>\n",
       "      <td>8.591106e+05</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.262102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.240044</td>\n",
       "      <td>0.724878</td>\n",
       "      <td>8.926562e+05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.275122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.230024</td>\n",
       "      <td>0.711404</td>\n",
       "      <td>9.248858e+05</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.288596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.220020</td>\n",
       "      <td>0.697409</td>\n",
       "      <td>9.678065e+05</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.302591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.210092</td>\n",
       "      <td>0.682934</td>\n",
       "      <td>1.005933e+06</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.317066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.200037</td>\n",
       "      <td>0.667695</td>\n",
       "      <td>1.043815e+06</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.332305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.190080</td>\n",
       "      <td>0.652000</td>\n",
       "      <td>1.089426e+06</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.348000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.180070</td>\n",
       "      <td>0.635553</td>\n",
       "      <td>1.134619e+06</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.364447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.170038</td>\n",
       "      <td>0.618359</td>\n",
       "      <td>1.182787e+06</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.381641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.160024</td>\n",
       "      <td>0.600441</td>\n",
       "      <td>1.240597e+06</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.399559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.581601</td>\n",
       "      <td>1.297501e+06</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.418399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.140093</td>\n",
       "      <td>0.562115</td>\n",
       "      <td>1.362230e+06</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.437885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.130046</td>\n",
       "      <td>0.541371</td>\n",
       "      <td>1.436043e+06</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.458629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.120059</td>\n",
       "      <td>0.519757</td>\n",
       "      <td>1.496978e+06</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.480243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.110055</td>\n",
       "      <td>0.497058</td>\n",
       "      <td>1.580531e+06</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.502942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.100256</td>\n",
       "      <td>0.473517</td>\n",
       "      <td>1.668694e+06</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.526483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0.090004</td>\n",
       "      <td>0.447467</td>\n",
       "      <td>1.770501e+06</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.552533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.080037</td>\n",
       "      <td>0.420595</td>\n",
       "      <td>1.877388e+06</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.579405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>0.070100</td>\n",
       "      <td>0.391985</td>\n",
       "      <td>2.015419e+06</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.608015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>0.060038</td>\n",
       "      <td>0.360832</td>\n",
       "      <td>2.170222e+06</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.639168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.050109</td>\n",
       "      <td>0.327506</td>\n",
       "      <td>2.381624e+06</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.672494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.040033</td>\n",
       "      <td>0.289902</td>\n",
       "      <td>2.668522e+06</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.710098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.030043</td>\n",
       "      <td>0.248190</td>\n",
       "      <td>2.980963e+06</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.751810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.020020</td>\n",
       "      <td>0.199343</td>\n",
       "      <td>3.652011e+06</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.800657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.010035</td>\n",
       "      <td>0.136323</td>\n",
       "      <td>5.108179e+06</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.863677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.600000e+06</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        AccQW  AccQQ_up            QQ  Quant  AccQQ_down\n",
       "0    1.000000  1.000000 -3.400000e+04   0.00    0.000000\n",
       "1    0.990051  1.001109 -3.119506e+04   0.01   -0.001109\n",
       "2    0.980136  1.001435 -1.600528e+04   0.02   -0.001435\n",
       "3    0.970714  1.001620 -1.026172e+04   0.03   -0.001620\n",
       "4    0.960013  1.001735 -4.304071e+03   0.04   -0.001735\n",
       "..        ...       ...           ...    ...         ...\n",
       "96   0.040033  0.289902  2.668522e+06   0.96    0.710098\n",
       "97   0.030043  0.248190  2.980963e+06   0.97    0.751810\n",
       "98   0.020020  0.199343  3.652011e+06   0.98    0.800657\n",
       "99   0.010035  0.136323  5.108179e+06   0.99    0.863677\n",
       "100  0.000000  0.000000  2.600000e+06   1.00    1.000000\n",
       "\n",
       "[101 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WealthDist_byQuant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f34f9c8ba8>,\n",
       " <matplotlib.lines.Line2D at 0x1f34f9c8d68>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(WealthDist_byQuant['Quant'],WealthDist_byQuant[['Quant','AccQQ_down']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f34f9f6e80>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "WealthDist_byHist.plot(kind='bar',x='QQ_mid',y='QW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>QW</th>\n",
       "      <th>QQ_QW</th>\n",
       "      <th>QQ_left</th>\n",
       "      <th>QQ_right</th>\n",
       "      <th>QQ_mid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.010637</td>\n",
       "      <td>-771.170804</td>\n",
       "      <td>-inf</td>\n",
       "      <td>-3.000000e+04</td>\n",
       "      <td>-3.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005007</td>\n",
       "      <td>-122.249476</td>\n",
       "      <td>-3.000000e+04</td>\n",
       "      <td>-2.033333e+04</td>\n",
       "      <td>-2.516667e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.012853</td>\n",
       "      <td>-193.636709</td>\n",
       "      <td>-2.033333e+04</td>\n",
       "      <td>-1.066667e+04</td>\n",
       "      <td>-1.550000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.020983</td>\n",
       "      <td>-110.048224</td>\n",
       "      <td>-1.066667e+04</td>\n",
       "      <td>-1.000000e+03</td>\n",
       "      <td>-5.833333e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.027640</td>\n",
       "      <td>8.700557</td>\n",
       "      <td>-1.000000e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.343616</td>\n",
       "      <td>21586.935274</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>2.009714e+05</td>\n",
       "      <td>1.009857e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.135620</td>\n",
       "      <td>39709.530170</td>\n",
       "      <td>2.009714e+05</td>\n",
       "      <td>4.009429e+05</td>\n",
       "      <td>3.009571e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.100493</td>\n",
       "      <td>50157.368112</td>\n",
       "      <td>4.009429e+05</td>\n",
       "      <td>6.009143e+05</td>\n",
       "      <td>5.009286e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.075214</td>\n",
       "      <td>52184.778199</td>\n",
       "      <td>6.009143e+05</td>\n",
       "      <td>8.008857e+05</td>\n",
       "      <td>7.009000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.056416</td>\n",
       "      <td>50522.944802</td>\n",
       "      <td>8.008857e+05</td>\n",
       "      <td>1.000857e+06</td>\n",
       "      <td>9.008714e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.044501</td>\n",
       "      <td>48698.641180</td>\n",
       "      <td>1.000857e+06</td>\n",
       "      <td>1.200829e+06</td>\n",
       "      <td>1.100843e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.032561</td>\n",
       "      <td>42198.020048</td>\n",
       "      <td>1.200829e+06</td>\n",
       "      <td>1.400800e+06</td>\n",
       "      <td>1.300814e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.026914</td>\n",
       "      <td>40224.722803</td>\n",
       "      <td>1.400800e+06</td>\n",
       "      <td>1.600771e+06</td>\n",
       "      <td>1.500786e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.020309</td>\n",
       "      <td>34482.269529</td>\n",
       "      <td>1.600771e+06</td>\n",
       "      <td>1.800743e+06</td>\n",
       "      <td>1.700757e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.015653</td>\n",
       "      <td>29599.532092</td>\n",
       "      <td>1.800743e+06</td>\n",
       "      <td>2.000714e+06</td>\n",
       "      <td>1.900729e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.012887</td>\n",
       "      <td>26978.384877</td>\n",
       "      <td>2.000714e+06</td>\n",
       "      <td>2.200686e+06</td>\n",
       "      <td>2.100700e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.009364</td>\n",
       "      <td>21458.355880</td>\n",
       "      <td>2.200686e+06</td>\n",
       "      <td>2.400657e+06</td>\n",
       "      <td>2.300671e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.006542</td>\n",
       "      <td>16330.141581</td>\n",
       "      <td>2.400657e+06</td>\n",
       "      <td>2.600629e+06</td>\n",
       "      <td>2.500643e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.007410</td>\n",
       "      <td>19987.605272</td>\n",
       "      <td>2.600629e+06</td>\n",
       "      <td>2.800600e+06</td>\n",
       "      <td>2.700614e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.005776</td>\n",
       "      <td>16779.244498</td>\n",
       "      <td>2.800600e+06</td>\n",
       "      <td>3.000571e+06</td>\n",
       "      <td>2.900586e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.003426</td>\n",
       "      <td>10552.894599</td>\n",
       "      <td>3.000571e+06</td>\n",
       "      <td>3.200543e+06</td>\n",
       "      <td>3.100557e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.002759</td>\n",
       "      <td>9123.069119</td>\n",
       "      <td>3.200543e+06</td>\n",
       "      <td>3.400514e+06</td>\n",
       "      <td>3.300529e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.002486</td>\n",
       "      <td>8720.978625</td>\n",
       "      <td>3.400514e+06</td>\n",
       "      <td>3.600486e+06</td>\n",
       "      <td>3.500500e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.002468</td>\n",
       "      <td>9147.733144</td>\n",
       "      <td>3.600486e+06</td>\n",
       "      <td>3.800457e+06</td>\n",
       "      <td>3.700471e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.001610</td>\n",
       "      <td>6270.173321</td>\n",
       "      <td>3.800457e+06</td>\n",
       "      <td>4.000429e+06</td>\n",
       "      <td>3.900443e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.001477</td>\n",
       "      <td>6054.057133</td>\n",
       "      <td>4.000429e+06</td>\n",
       "      <td>4.200400e+06</td>\n",
       "      <td>4.100414e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.001776</td>\n",
       "      <td>7619.662572</td>\n",
       "      <td>4.200400e+06</td>\n",
       "      <td>4.400371e+06</td>\n",
       "      <td>4.300386e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.001127</td>\n",
       "      <td>5083.197532</td>\n",
       "      <td>4.400371e+06</td>\n",
       "      <td>4.600343e+06</td>\n",
       "      <td>4.500357e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.001255</td>\n",
       "      <td>5889.924475</td>\n",
       "      <td>4.600343e+06</td>\n",
       "      <td>4.800314e+06</td>\n",
       "      <td>4.700329e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.000873</td>\n",
       "      <td>4299.559064</td>\n",
       "      <td>4.800314e+06</td>\n",
       "      <td>5.000286e+06</td>\n",
       "      <td>4.900300e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.000709</td>\n",
       "      <td>3628.929958</td>\n",
       "      <td>5.000286e+06</td>\n",
       "      <td>5.200257e+06</td>\n",
       "      <td>5.100271e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.000795</td>\n",
       "      <td>4213.697101</td>\n",
       "      <td>5.200257e+06</td>\n",
       "      <td>5.400229e+06</td>\n",
       "      <td>5.300243e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.000358</td>\n",
       "      <td>1968.628309</td>\n",
       "      <td>5.400229e+06</td>\n",
       "      <td>5.600200e+06</td>\n",
       "      <td>5.500214e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.000646</td>\n",
       "      <td>3674.173521</td>\n",
       "      <td>5.600200e+06</td>\n",
       "      <td>5.800171e+06</td>\n",
       "      <td>5.700186e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.000659</td>\n",
       "      <td>3909.803557</td>\n",
       "      <td>5.800171e+06</td>\n",
       "      <td>6.000143e+06</td>\n",
       "      <td>5.900157e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.000340</td>\n",
       "      <td>2080.662056</td>\n",
       "      <td>6.000143e+06</td>\n",
       "      <td>6.200114e+06</td>\n",
       "      <td>6.100129e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.000334</td>\n",
       "      <td>2094.650019</td>\n",
       "      <td>6.200114e+06</td>\n",
       "      <td>6.400086e+06</td>\n",
       "      <td>6.300100e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.000275</td>\n",
       "      <td>1792.558518</td>\n",
       "      <td>6.400086e+06</td>\n",
       "      <td>6.600057e+06</td>\n",
       "      <td>6.500071e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.000245</td>\n",
       "      <td>1638.083981</td>\n",
       "      <td>6.600057e+06</td>\n",
       "      <td>6.800029e+06</td>\n",
       "      <td>6.700043e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.000533</td>\n",
       "      <td>3687.298064</td>\n",
       "      <td>6.800029e+06</td>\n",
       "      <td>7.000000e+06</td>\n",
       "      <td>6.900014e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.005450</td>\n",
       "      <td>65060.305866</td>\n",
       "      <td>7.000000e+06</td>\n",
       "      <td>inf</td>\n",
       "      <td>7.000000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             QW         QQ_QW       QQ_left      QQ_right        QQ_mid\n",
       "Group                                                                  \n",
       "0      0.010637   -771.170804          -inf -3.000000e+04 -3.000000e+04\n",
       "1      0.005007   -122.249476 -3.000000e+04 -2.033333e+04 -2.516667e+04\n",
       "2      0.012853   -193.636709 -2.033333e+04 -1.066667e+04 -1.550000e+04\n",
       "3      0.020983   -110.048224 -1.066667e+04 -1.000000e+03 -5.833333e+03\n",
       "4      0.027640      8.700557 -1.000000e+03  1.000000e+03  0.000000e+00\n",
       "5      0.343616  21586.935274  1.000000e+03  2.009714e+05  1.009857e+05\n",
       "6      0.135620  39709.530170  2.009714e+05  4.009429e+05  3.009571e+05\n",
       "7      0.100493  50157.368112  4.009429e+05  6.009143e+05  5.009286e+05\n",
       "8      0.075214  52184.778199  6.009143e+05  8.008857e+05  7.009000e+05\n",
       "9      0.056416  50522.944802  8.008857e+05  1.000857e+06  9.008714e+05\n",
       "10     0.044501  48698.641180  1.000857e+06  1.200829e+06  1.100843e+06\n",
       "11     0.032561  42198.020048  1.200829e+06  1.400800e+06  1.300814e+06\n",
       "12     0.026914  40224.722803  1.400800e+06  1.600771e+06  1.500786e+06\n",
       "13     0.020309  34482.269529  1.600771e+06  1.800743e+06  1.700757e+06\n",
       "14     0.015653  29599.532092  1.800743e+06  2.000714e+06  1.900729e+06\n",
       "15     0.012887  26978.384877  2.000714e+06  2.200686e+06  2.100700e+06\n",
       "16     0.009364  21458.355880  2.200686e+06  2.400657e+06  2.300671e+06\n",
       "17     0.006542  16330.141581  2.400657e+06  2.600629e+06  2.500643e+06\n",
       "18     0.007410  19987.605272  2.600629e+06  2.800600e+06  2.700614e+06\n",
       "19     0.005776  16779.244498  2.800600e+06  3.000571e+06  2.900586e+06\n",
       "20     0.003426  10552.894599  3.000571e+06  3.200543e+06  3.100557e+06\n",
       "21     0.002759   9123.069119  3.200543e+06  3.400514e+06  3.300529e+06\n",
       "22     0.002486   8720.978625  3.400514e+06  3.600486e+06  3.500500e+06\n",
       "23     0.002468   9147.733144  3.600486e+06  3.800457e+06  3.700471e+06\n",
       "24     0.001610   6270.173321  3.800457e+06  4.000429e+06  3.900443e+06\n",
       "25     0.001477   6054.057133  4.000429e+06  4.200400e+06  4.100414e+06\n",
       "26     0.001776   7619.662572  4.200400e+06  4.400371e+06  4.300386e+06\n",
       "27     0.001127   5083.197532  4.400371e+06  4.600343e+06  4.500357e+06\n",
       "28     0.001255   5889.924475  4.600343e+06  4.800314e+06  4.700329e+06\n",
       "29     0.000873   4299.559064  4.800314e+06  5.000286e+06  4.900300e+06\n",
       "30     0.000709   3628.929958  5.000286e+06  5.200257e+06  5.100271e+06\n",
       "31     0.000795   4213.697101  5.200257e+06  5.400229e+06  5.300243e+06\n",
       "32     0.000358   1968.628309  5.400229e+06  5.600200e+06  5.500214e+06\n",
       "33     0.000646   3674.173521  5.600200e+06  5.800171e+06  5.700186e+06\n",
       "34     0.000659   3909.803557  5.800171e+06  6.000143e+06  5.900157e+06\n",
       "35     0.000340   2080.662056  6.000143e+06  6.200114e+06  6.100129e+06\n",
       "36     0.000334   2094.650019  6.200114e+06  6.400086e+06  6.300100e+06\n",
       "37     0.000275   1792.558518  6.400086e+06  6.600057e+06  6.500071e+06\n",
       "38     0.000245   1638.083981  6.600057e+06  6.800029e+06  6.700043e+06\n",
       "39     0.000533   3687.298064  6.800029e+06  7.000000e+06  6.900014e+06\n",
       "40     0.005450  65060.305866  7.000000e+06           inf  7.000000e+06"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WealthDist_byHist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wealth-Income Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.167815395617627"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QW_Mean(DS,Var_Wealth,'StatWeight')/QW_Mean(DS,Var_Income,'StatWeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Liquid Net Worth - Income Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.905698471819156"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QW_Mean(DS,'Asset_Liquid','StatWeight')/QW_Mean(DS,Var_Income,'StatWeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "DS['Ratio_LiquidAsset_Income'] = DS['Asset_Liquid']/DS['Income_Market']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.175975824625618"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QW_Mean(DS.loc[np.isfinite(DS['Ratio_LiquidAsset_Income']),:],'Ratio_LiquidAsset_Income','StatWeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Liquidity to Income Ratio (Annual Income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.905698471819156"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QW_Mean(DS,'Asset_Liquid','StatWeight')/QW_Mean(DS,Var_Income,'StatWeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fraction of HtM Households"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3694585244186982"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(DS['Ratio_LiquidAsset_Income']<1/12/2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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